import gradio as gr import random from PIL import Image from funcs import detect_emotions, cosine_distance, generate_triggers_img, get_doc_response_emotions, summarize_and_recommend with gr.Blocks() as demo: gr.Markdown("""# Your Intimate Personal Therapist ## Your Personal AI Therapist. Start chatting... Once you finish chatting, click "Generate Session Notes & Recommendations" button and wait for few seconds for them to appear""") therapy_session_conversation = gr.State([]) img_paths = ['2.jpg','3.jpeg','4.jpeg','5.jpeg','6.jpeg'] img_path = random.choice(img_paths) img_1 = Image.open(img_path) img_path = '7.jpg' img_2 = Image.open(img_path) with gr.Row(): with gr.Column(scale=1): image = gr.Image(type='pil', label ='Your Personal Therapy Assistant', value=img_1, interactive=False) emotions = gr.Image(value=img_2, label='Top 5 Emotion Triggers') summary_notes = gr.Textbox(label="Summary Notes of the Session", visible=False) with gr.Column(scale=2): chatbox = gr.Chatbot(label="Therapy Session Conversation",value =[[None, 'Therapist: Hello, What can I do for you?']], height=300) user_input = gr.Textbox(placeholder="Enter your message here...", label="User") submit_button = gr.Button("Submit") submit_button.click(get_doc_response_emotions, [user_input, chatbox], [user_input, chatbox, emotions]) user_input.submit(get_doc_response_emotions, [user_input, chatbox], [user_input, chatbox, emotions]) recommendations = gr.Textbox(label="Recommended Actions", visible = False) def summarize_and_recommend_process(tcs): sn, r, cb = summarize_and_recommend(tcs) return gr.update(visible=True, value=sn), gr.update(visible=True, value=r), gr.update(visible=True, value=cb) process_button = gr.Button("Generate Session Notes & Recommendations") clear = gr.ClearButton(components=[user_input, chatbox, emotions, summary_notes, recommendations], value="Clear console") process_button.click(summarize_and_recommend_process, inputs=[chatbox], outputs=[summary_notes, recommendations, chatbox]) demo.launch(debug=True, share=True)